A Large-Scale Ensemble Learning Framework for Demand Forecasting. - Young-Jin Park; Donghyun Kim; Frédéric Odermatt; Juho Lee; Kyung-Min Kim (ICDM, 2022)

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators. - Malte J. Rasch; Charles Mackin; Manuel Le Gallo; An Chen 002; Andrea Fasoli; Frédéric Odermatt; Ning Li; S. R. Nandakumar; Pritish Narayanan; Hsinyu Tsai; Geoffrey W. Burr; Abu Sebastian; et al. (CoRR, 2023)

Cascaded Beam Search: Plug-and-Play Terminology-Forcing For Neural Machine Translation. - Frédéric Odermatt; Béni Egressy; Roger Wattenhofer (CoRR, 2023)

Gradient descent-based programming of analog in-memory computing cores. - Julian Büchel; Athanasios Vasilopoulos; Benedikt Kersting; Frédéric Odermatt; Kevin Brew; Injo Ok; Samuel Choi; Iqbal Saraf; Victor Chan; Timothy Philip; Nicole Saulnier; Vijay Narayanan; et al. (CoRR, 2023)

A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting. - Young-Jin Park; Donghyun Kim; Frédéric Odermatt; Juho Lee; Kyung-Min Kim (CoRR, 2024)

A comparison of translation performance between DeepL and Supertext. - Alex Flückiger; Chantal Amrhein; Tim Graf; Frédéric Odermatt; Martin Pömsl; Philippe Schläpfer; Florian Schottmann; Samuel Läubli (CoRR, 2025)